Zuofeng Shang

Zuofeng Shang is an Associate Professor in the Department of Mathematical Sciences at New Jersey Institute of Technology (NJIT). His research focuses on advanced statistical methodologies, including Bayesian statistics, nonparametric learning, and deep learning applications for complex data. He has received multiple National Science Foundation grants for projects addressing statistical guarantees in massive data analysis, sleep disorder management, and scalable learning algorithms.

Shang's work spans theoretical foundations (e.g., minimax optimality, spline smoothing) and applied domains (e.g., financial data integration, ODE calibration via neural networks). His collaborations include interdisciplinary projects with medical researchers to improve obstructive sleep apnea management. Notable contributions include developing fast tensor completion algorithms and deep learning frameworks for functional data analysis.

He has authored over 45 peer-reviewed publications and actively participates in academic outreach, as evidenced by media coverage of his research advancements in statistical methodologies.